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Sebastiani, Fabrizio. 2002. "Machine Learning In Automated Text Categorization." ACM Computing Surveys, Vol. 34, No. 1, Pp. 1-47.pdf

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Preview of Sebastiani, Fabrizio. 2002. "Machine learning in automated text categorization." ACM Computing Surveys, vol. 34, no. 1, pp. 1-47
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📊 Size: 496 KB
📄 Pages: 47 pages
⬇️ Downloads: 84

Summary

The automated categorization of texts into predefined categories has gained significant interest due to the increased availability of digital documents and the need to organize them. Machine learning techniques have become the dominant approach, automatically building a classifier by learning from preclassified documents. This approach offers good effectiveness, saves expert labor power, and is portable to different domains. The survey discusses main approaches to text categorization within the machine learning paradigm, focusing on document representation, classifier construction, and classifier evaluation. Text categorization is a task at the crossroads of machine learning and information retrieval, sharing characteristics with information extraction and text mining. The paper defines text categorization, reviews its applications, and describes the machine learning approach, text indexing, classifier construction, and evaluation.

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The automated categorization of texts into predefined categories has gained significant interest due to the increased availability of digital documents and the...

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  • File Format: PDF
  • File Size: 496 KB
  • Pages: 47
  • Language: EN
  • Total Downloads: 84
  • Last Updated: 6 hours ago

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